With the rapid development of Internet, the amount of data has increased exponentially. Due to information overload, it is difficult for users to single out the ones they are interested in from a large number of choices [1]}, [2]}. In order to make it easier for users to obtain the items and information they need, recommendation systems have been applied to scenarios such as music recommendation [3]}, movie recommendation [4]}, [5]}, and online shopping [6]}, [7]}.
The recommendation algorithm is the core of recommendation system. One widely used recommendation algorithm is collaborative filtering (CF), which assigns representation vectors based on users' IDs and items' IDs [1]}. Then it models their interaction through specific operations (such as inner product [9]} or neural network [10]}). In addition, methods based on CF are often plagued by the sparsity of users interaction with items and cold start problem. In order to solve these limitations, researchers usually turn to feature-rich scenarios, where the attributes of users and items are applied to compensate for the sparsity and improve performance of recommendation systems [11]}, [12]}, [13]}, [14]}.
